Bioconductor Epigrahmm
EpigraHMM provides a set of tools for the analysis of epigenomic data based on hidden...
Install
npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-epigrahmm -g -a claude-code -y- Repository
- bioMate-AI/biomate-bioconductor-kb793
- Category
- Engineering
- Updated
- 5mo ago
- License
- See repository
- Context cost
- Mediumabout 1,526 tokens
- Runs scripts
- No, instructions only
What it does
epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
epigraHMM
Workflows
Differential Peak Calling
Detect differential enrichment regions (peaks) across multiple experimental conditions and classify peaks into combinatorial patterns of enrichment.
library(epigraHMM)
# Define input BAM files and metadata
bamFiles <- system.file(
package = "genomationData",
"extdata",
c("wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam",
"wgEncodeBroadHistoneH1hescSuz12051317AlnRep1.chr21.bam")
)
colData <- data.frame(condition = c("CTCF", "SUZ12"), replicate = c(1, 1))
# Create dataset object
object_differential <- epigraHMMDataSetFromBam(
bamFiles = bamFiles,
colData = colData,
genome = 'hg19',
windowSize = 500,
gapTrack = TRUE,
blackList = TRUE
)
# Set up EM control and run differential peak calling
control <- controlEM(fileName = 'control-differential', criterion = 'MACPE')
object_differential <- normalizeCounts(object_differential, control)
object_differential <- initializer(object_differential, control)
object_differential <- epigraHMM(object = object_differential, control = control, type = 'differential')
# Call peaks
peaks_differential <- callPeaks(object = object_differential)
Input: BAM files and sample metadata; Output: A GRanges object containing called differential peak regions.
Standard Workflow
Detect consensus enrichment regions (peaks) across technical or biological replicates of a single experimental condition.
library(epigraHMM)
# Define input BAM files and metadata
bamFiles <- system.file(
package = "genomationData",
"extdata",
"wgEncodeBroadHistoneH1hescCtcfStdAlnRep1.chr21.bam"
)
colData <- data.frame(condition = "CTCF", replicate = 1)
# Create dataset object
object_consensus <- epigraHMMDataSetFromBam(
bamFiles = bamFiles,
colData = colData,
genome = 'hg19',
windowSize = 500,
gapTrack = TRUE,
blackList = TRUE
)
